Multi-domain neighborhood embedding and weighting of sampled data

ABSTRACT

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/080,822, filed Oct. 26, 2020, which claims the benefit of U.S.Provisional Patent Application No. 62/928,572, filed Oct. 31, 2019, thedisclosures of which are incorporated in their entireties by referenceherein.

BACKGROUND

Some automotive systems include sophisticated sensor equipmentconfigured to capture and process point cloud data for representing andinterpreting semantics of a three-dimensional (3D) scene. Point clouddata (e.g., lidar data, radar data, ultrasound data, camera data, othersensor data) obtained in furtherance of many common automotive tasks,for example, shape segmentation or indoor scenario parsing, is typicallymuch denser than other point cloud data normally acquired whileinterpreting semantics of an outdoor scene. Although many ways toprocess point cloud data exist, their effectiveness depreciates as thepoint cloud data becomes less dense.

SUMMARY

This document describes point cloud processing through “Multi-domainNeighborhood Embedding and Weighting” (MNEW) for use in processing pointcloud data, including sparsely populated data obtained from a lidar, acamera, a radar, or combination thereof. MNEW is a process based on adilation architecture that captures pointwise and global features of thepoint cloud data involving multi-scale local semantics adopted from ahierarchical encoder-decoder structure. Neighborhood information isembedded in both static geometric and dynamic feature domains. Ageometric distance, feature similarity, and local sparsity can becomputed and transformed into adaptive weighting factors that arereapplied to the point cloud data. This enables an automotive system toobtain outstanding performance with sparse and dense point cloud data.Processing point cloud data via the MNEW techniques promotes greateradoption of sensor-based autonomous driving and perception-basedsystems.

In one example, a method includes obtaining point cloud data from one ormore sensors of a vehicle, determining geometric properties of the pointcloud data by at least determining geometric distances between points inthe point cloud data, and determining feature properties of the pointcloud data including feature similarities of the points. The methodfurther includes modifying the point cloud data by embedding arespective weighting at each point in the point cloud data, therespective weighting being based on: the geometric distances that arebetween that point and other points in the point cloud data; a sparsityof the point cloud data at that point; and the feature similarities ofthat point and the other points in the point cloud data. The methodfurther includes responsive to modifying the point cloud, executing avehicle operation using the respective weightings embedded in themodified point cloud data.

This document a system includes a processor configured to perform theabove-summarized method, as well as, describing means for performing theabove-summarized method and other methods set forth herein, in additionto describing methods performed by the above-summarized systems andmethods performed by other systems set forth herein.

This summary introduces simplified concepts of multi-domain neighborhoodembedding and weighting for point cloud processing, which are furtherdescribed below in the Detailed Description and Drawings. This summaryis not intended to identify essential features of the claimed subjectmatter, nor is it intended for use in determining the scope of theclaimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more aspects of multi-domain neighborhoodembedding and weighting for point cloud processing are described in thisdocument with reference to the following figures. The same numbers areoften used throughout the drawings to reference like features andcomponents.

FIG. 1 illustrates an example of an environment in which multi-domainneighborhood embedding and weighting for point cloud processing isperformed by an automotive system.

FIG. 2 illustrates an example process to modify point cloud data usingmulti-domain neighborhood embedding and weighting.

FIG. 3 illustrates in an example of a module configured to process pointclouds through multi-domain neighborhood embedding and weighting.

FIG. 4 illustrates an example method for processing point clouds throughmulti-domain neighborhood embedding and weighting.

FIG. 5-1 illustrates an example of a dense point cloud processed by anautomotive system configured to process point clouds using multi-domainneighborhood embedding and weighting.

FIG. 5-2 illustrates an example of a sparse point cloud processed by anautomotive system configured to process point clouds using multi-domainneighborhood embedding and weighting.

FIG. 6-1 illustrates an example point cloud distribution and performancevariation by distance.

FIG. 6-2 illustrates an example point cloud distribution and performancevariation by sparsity.

DETAILED DESCRIPTION

Overview

Point cloud data is preferred for executing some autonomous driving orother vehicle perception-related tasks, for example, three-dimensionalshape recognition, scene segmentation, indoor scenario parsing, andoutdoor scene interpretation on a large-scale. Lidar point clouds, whencompared against point cloud data generated from other types of sensors,for example, camera and radar, have an advantage in vehicle-perceptionbecause lidar point clouds can capture both real distance measurementsand fine semantic descriptions. In research areas related to computervision, studies about point clouds are most concerned with dense datasets. For example, topics for example: three-dimensional shaperecognition, part segmentation, indoor scenario parsing, and large-scaleoutdoor scene interpretations are studied using densely populated pointclouds or data sets. Several benchmark data sets, including ModelNet40,ShapeNet, S3DIS, and Semantic3D, have been established to testperformance related to these topics. These data sets have spatialproperties that are unlike spatial properties of actual data obtainedduring real-world sensor scans (e.g., by an on-board lidar or anothervehicle-mounted sensor). Furthermore, point cloud data tends to be evensparser when generated from a real-world sweep of a vehicle'ssurrounding environment as compared to an interior scan of a cabin; thisis because sparsity of a point cloud increases as distance to an objectbecomes greater.

There have generally been three different approaches to processing pointclouds. A first approach is to generate a projection fromtwo-dimensional (2D) representations; the basic idea of this approach isto transform and stitch together unstructured point clouds into 2Dimages, which can be directly input into an image-based convolutionalneural network. This works for a fusion-sensor system that combinessensor data from multiple different sources. A drawback to these 2Dprojections is a potential loss of information when a large number ofpoints are occluded from a field-of-view.

Another approach involves voxelization into 3D volumetric grids of cellsor a structural variation thereof (e.g., octree, spherical). Although 3Dvoxelization is better at maintaining points in a 3D space, a 3Dconvolution may suffer a similar loss of information due to occlusions,and a 3D convolution can also be computationally expensive. There mayalso be processing difficulty when working with a 3D convolution.

Thirdly, a fundamental model called PointNet can process an unorderedand unstructured point cloud, which inspires many other studies toresearch point cloud classification and segmentation. However,performance is only reported against several dense data benchmarks andtheir effectiveness for processing sparse data clouds is unknown.

VirtualKITTI, SemanticKITTI, and DeepenaiKITTI are some publiclyavailable datasets that provide semantic labels for sparse point clouds.A comparison of the above-three approaches to directly processing rawpoint clouds reveals their ineffectiveness on processing sparse data.The network architecture, the neighbor points collection method, andlocal sparsity are the essential factors that cause changes to each oftheir performance.

In contrast to other approaches to point cloud processing andsegmentation, this document describes Multi-domain NeighborhoodEmbedding and Weighting (MNEW), which is a process of embedding weightsin point cloud data based on geometric distance, feature similarity,and/or neighborhood sparsity, no matter if the point cloud data isdensely or sparsely populated. Particular point-wise details and globalsemantics are captured from the point cloud data, which is then appliedto the point cloud data during a multi-scale local neighborhoodsegmentation process that executes in a static geometric domain and adynamic feature space. These MNEW techniques, can configure anautomotive system to achieve top performance processing sparselypopulated point cloud data and dense data alike, including meeting orexceeding various state-of-the-art performance benchmarks (e.g.,Semantic3D, S3DIS) that test performance against dense point cloud data.

Example Environments

FIG. 1 illustrates an example of an environment 100 in whichmulti-domain neighborhood embedding and weighting for point cloudprocessing is performed by an automotive system 104. In the depictedenvironment 100, the automotive system 104 is mounted to, or integratedwithin, a vehicle 102 that can travel on a roadway. The automotivesystem 104 can embed weightings in point cloud data generated by one ormore sensors 106 onboard the vehicle 102. Although illustrated as a car,the vehicle 102 can represent other types of motorized vehicles (e.g., amotorcycle, a bus, a tractor, a semi-trailer truck, or constructionequipment). In general, manufacturers can mount the automotive system104 to any moving platform that can travel.

In the depicted implementation, a portion of the automotive system 104is mounted into a taillight module or bumper. The automotive system 104can project a field-of-view from any exterior surface of the vehicle102. For example, vehicle manufacturers can integrate at least a part ofthe automotive system 104 into a rearview mirror, a side mirror, roof,or any other interior or exterior location. In general, vehiclemanufacturers can design the location of the automotive system 104 toprovide a particular field-of-view that sufficiently encompasses aroadway and surrounding environment in which the vehicle 102 may betraveling. The automotive system 104 includes one or more sensors 106, aprocessor 108, and a non-transitory computer-readable storage media 110.

The processor 108 can be a microprocessor or a system-on-chip of acomputing device. The processor 108 executes computer-executableinstructions stored within the non-transitory computer-readable storagemedia 110. As an example, the processor 108 can execute the MNEW module114 and the sensor interface 112 to modify point cloud data for use inoperating the vehicle 102. The processor 108 can also retrieve or storepoint cloud data from or to the non-transitory computer-readable storagemedia 110.

The sensors 106 generate point cloud data, including sparse and densepoint cloud data. The point cloud data generated by the sensors 106 isobtained by a processor 108. The processor 108 receives the point clouddata as input and processes the data to embed weightings in the datathat enable more efficient downstream processing by another system ofthe vehicle 102, for example, an object detection system or anautonomous or semiautonomous navigation system (neither is shown). Thesensors 106 can include a camera, a radar system, a global positioningsystem (GPS), a lidar system, or any combination thereof. A camera cantake photographic images or video of the road. A radar system or a lidarsystem can use electromagnetic signals to detect objects in the road. AGPS can determine a position and/or navigation of the vehicle 102.

The non-transitory computer-readable storage media 110 maintainsinstructions that when executed by the processor 108, cause theautomotive system 104 to perform an operation or function. Thenon-transitory computer-readable storage media 110 can also store datafor processing and processed data. For example, the non-transitorycomputer-readable storage media 110 includes a sensor interface 112, aMNEW module 114, a raw data store 116, and a segmented data store 118.When executed by the processor 108, the sensor interface 112 receivespoint cloud data generated by the sensors 106 and stores the point clouddata in the raw data store 116. The processor 108 may execute the MNEWmodule 114 to process contents of the raw data store 116 and producemodified or segmented data for the segmented data store 118. Thisdocument describes the operations of the MNEW module 114 of theautomotive system 104 in greater detail with respect to FIG. 2 .

FIG. 2 illustrates an example process 200 to modify point cloud datausing multi-domain neighborhood embedding and weighting. The MNEW module114, when executed by the processor 108, obtains information containedin the raw data store 116 as input, and outputs segmented data for thesegmented data store 118. The process 200 inherits aspects of thePointNet model mentioned above, which is able to capture pointwisedetails and global semantics in point cloud data. Going beyond PointNet,however, the MNEW module 114 enables point cloud data to embody localcontextual information. In this way, unlike PointNet, the MNEW module114 makes even sparse point cloud data usable for vehicle control orobject detection. The process 200 excludes or refrains from subsamplingand interpolation processing of point cloud data, which differs fromprevious systems that process point cloud data based on a hierarchicalencoder-decoder structure.

The MNEW module 114 is configured to process raw point cloud data fromthe raw data store 116 by collecting static geometry neighbors andfurther collecting dynamic feature neighbors. For a query point (e.g., astar) within a set of point clouds (e.g., dots), the MNEW module 114groups geometry neighbors based on distance (e.g., Euclid distance)towards a query point. This separation by distance (represented as tworadius) is shown in FIG. 2 in the upper branch of the process 200.Through grouping feature neighbors based on similarities (e.g., usingmultiple k-nearest neighbors algorithms) that change across differentnetwork layers (as illustrated in the lower branch of the process 200),the MNEW module 114 generates a feature (dynamic) neighborhood domain202-2. By grouping the geometry neighbors, the MNEW module 114 producesa geometry (static) neighborhood domain 202-1. Sparsity 204-1 and 204-2of each neighbor point is computed in the geometry and feature domains202-1, 202-2. A geometry sparsity 204-1 and a feature sparsity 204-2 ateach neighbor point can be transformed into weighting factors 206-1 and206-2. For each of the neighborhood points, the MNEW module 114indicates a distance and similarity, which can then be used as weightingfactors 206-1 and 206-2 that are applied to the raw point cloud datafrom the raw data store 116.

Applying the weighting factors 206-1 and 206-2 to the raw point clouddata produces modified (e.g., segmented) point cloud data for thesegmented data store 118, which adopts the point cloud data for multiplescaling in multiple domains. In this way, while some other techniquesfor processing point cloud data compute geometry distance or featuresimilarity as fixed weighting factors, and while still others transformlocal sparsity as a machine learned variable, the MNEW module 114 treatsgeometry distance, feature similarity, and local contextual informationincluding local sparsity each as trainable features of point cloud datathat can be applied as embeddings and weightings. This is distinct withother techniques where neighbors, if even collected, are collected inonly a single domain (e.g., either geometry or feature but not both).

FIG. 3 illustrates in an example of a module configured to process pointclouds through multi-domain neighborhood embedding and weighting. MNEWmodule 114-1 is an example of the MNEW module 114. The MNEW module 114receives point cloud data as input from the raw data store 116. Theoutput from the MNEW module 114 is saved as segmented data including theraw point cloud data, having been embedded with weightings, including asegmentation score.

The overall architecture inherits a dilated structure like PointNet,which excludes down-sampling and up-sampling operations for changingresolution. Through access to the raw data store 116, the MNEW module114-1 obtains (e.g., in batches) input N_(p) points, and passes througha sequence of multiple MNEW submodules 300-1, 300-2, and 300-3 toextract pointwise features L₁, L₂, and L₃ in a hierarchical order. ThreeMNEW submodules are used in this example, however, additional, or fewerMNEW submodules may be used in other examples. Note that localneighborhood information is carried forward through the MNEW module114-1, and through the MNEW submodules 300-1, 300-2, 300-3 (collectively“MNEW submodules 300”), which yields an improvement over PointNet andother techniques that cannot carry local neighborhood informationthrough to the end.

The MNEW module 114-1 increases a quantity of neighbors in L₁, L₂, andL₃, but maintains a quantity of query points unchanged. The point clouddata stored at the raw data store 116 includes a same quantity ofsampled points after being modified and stored as point cloud data atthe segmented data store 118.

Following the MNEW modules 300, the MNEW module 114-1 includes a global2D convolution and max pooling module 302, and two fully convolutionlayers 304-1, 304-2 are followed to aggregate the global feature G₃. Thehierarchical pointwise features and tiled global feature areconcatenated as a descriptor for each point and passed through multiple(e.g., three) regular 1D convolutions 306-1, 306-2, 306-3 to get thesegmentation score. The segmentation score can indicate a probability ordegree of likelihood associated with one or more possible, pointwiseclassifications.

An exploded view 310 of each of the MNEW submodules 300 is shown insidethe MNEW module 114-1. As drawn in the exploded view 310, the input ofeach of the MNEW submodules 300 includes batches of points with their x,y, z coordinates and original features, shaped as [B, N_(p),D_(xyz)+D_(fea)]. The MNEW submodules 300 compute pairwise distances inboth the geometry and feature domains, resulting in two matrices 312-1,312-2, each with shape [B, Np, Np] representing, respectively, thegeometry distance and feature similarity between every two points.

Radius-based geometry neighborhood selections are typically more robustthan k-nearest neighbor in a non-uniform sampling of cloud data.However, feature neighborhoods may dynamically shift and be hard toencircle. Therefore, multiple radii are used the geometry domain andmultiple k-nearest neighbor algorithms are used in the feature domain tocollect multiple scales of neighbor indices.

The MNEW submodules 300 each gather original features to compose twoinitial neighborhood embedding matrices X_(g) ⁰ and X_(f) ⁰, each with ashape [B, N_(p), N_(ng), D_(embed)] and [B, N_(p), N_(nf), D_(embed)],where ng=Σr_(i) representing the number of accumulated neighbors inmultiple-radius geometry space, and nf=Σk_(i) representing the number ofaccumulated neighbors in multiple k-nearest neighbor feature space. Eachelement is embedded in the matrices X_(g) ⁰ and X_(f) ⁰, as shown belowin Equation 1:f(x _(i) ,n _(j))=f(x _(n) _(j) ,x _(n) _(j) −x _(i)),x _(i,n) _(j) ∈(X_(g) ⁰ ∪X _(f) ⁰)  Equation 1In Equation 1, x_(i) denotes the i^(th) query point, and x_(n) _(j)denotes the j^(th) neighboring points. Because the geometry distancematrix D_(g) (shape [B, N_(p), N_(ng), 1]) and feature similarity matrixD_(f) (shape [B, N_(p), N_(ng), 1], represented by pairwise distances inthe feature space) are already computed and gathered, a transformationfunction T(d_(i), n_(j))=w_(i), n_(j)·f(d_(i), n_(j)) can be applied toobtain learning-based (w_(i), n_(j)) attention weights and apply onX_(g) ⁰ and X_(f) ⁰, resulting with attended embedding X_(g) ^(a) andX_(f) ^(a), as shown in Equation 2:X _(g) ^(a) =T(D _(g))·X _(g) ⁰X _(f) ^(a) =T(D _(g))·X _(f) ⁰  Equation 2The neighborhood sparsity can next be calculated using Equations 3 and 4that follow:

$\begin{matrix}{{{\mathbb{P}}\left( {x_{i},{n_{j}❘\mu},\sigma^{2}} \right)} = {\frac{1}{\sqrt{2^{{\pi\sigma}^{2}}}}{\exp\left\lbrack {- \frac{\left( {x_{n_{j} -}x_{i}} \right)^{2}}{2\sigma^{2}}} \right\rbrack}}} & {{Equation}3}\end{matrix}$ $\begin{matrix}{{S\left( {x_{i},{n_{j}❘\mu},\sigma^{2}} \right)} = \left( {\frac{1}{N_{n}}{\log\left\lbrack {\sum\limits_{n_{j} \in N_{n}}{{\mathbb{P}}\left( {x_{i},{n_{j}❘\mu},\sigma^{2}} \right)}} \right\rbrack}} \right)^{- 1}} & {{Equation}4}\end{matrix}$In Equation 3,

(x_(i), n_(j)|μ, σ²) is similar to a Gaussian probability densityfunction computed for every neighbors x_(n) _(j) with respect to thequery point x_(i). In Equation 4, S(x_(i), n_(j)) is the estimatedsparsity, which is an inverse of the average density, where we also takethe log-scale value to obtain a better sparsity distribution. Geometrysparsity S_(g) and feature sparsity S_(f) are computed separately, whichare transformed as the weighting factor for the activation of the 2Dconvolution. The weighted outputs X_(g) ^(w) (shape [B, N_(p), N_(ng),D_(conv)]) and X_(f) ^(w) (shape [B, N_(p), N_(ng), D_(conv)]) are givenby Equations 5:X _(g) ^(w) =T(S _(g))·h(X _(g) ^(a))X _(f) ^(w) =T(S _(f))·h(X _(f) ^(a))  Equations 5

After concatenating the neighborhood information from the geometry andfeature domain, an average pooling operation is followed to aggregate afeature vector for each query point, which yields the output of the MNEWsubmodules 300 as X_(mnew) ^(pit) with shape [B, N_(p), N_(ng),D_(conv)], as shown in Equation 6:

$\begin{matrix}{X_{mnew}^{out} = {\frac{1}{N_{p}}{\sum\limits_{i \in N_{p}}\left( {X_{g}^{w} \oplus X_{f}^{w}} \right)}}} & {{Equation}6}\end{matrix}$A loss function may be used, which is a combination of softmaxcross-entropy loss

_(CE) and adjusted regularization loss

_(Reg) by λ. Because the task is semantic segmentation only (i.e., noinstance-level labels), there is little to no discriminative loss, asshown in Equation 7:

_(Total)−

_(Total)=

_(CE)+λ

_(Reg)  Equation 7

In this way, the MNEW module 114-1 refrains from performing subsamplingand interpolation processing, which differs from other point cloud dataprocessing, for instance, that which is based on a hierarchicalencoder-decoder structure.

Compared with PointNet whose feature contains pointwise and globalinformation, the MNEW module 114-1 also include local neighborhoodfeatures. Unlike other techniques that collect neighbors in featurespace only, the MNEW module 114-1 embeds neighbor points in both thegeometry and feature domains. In terms of the neighborhood embedding,the MNEW module 114-1 adopts multi-scaling in multiple domains. Thisdistinct with all other processes for handling point cloud data whereneighbors are collected in only a single domain (e.g., either geometryor feature but not both). There may exist overlapping points selected bythe MNEW module 114-1 in both geometry and feature neighborhoods.However, the multiple-domain distance and sparsity are calculated andtransformed separately, which yield different weighting effects for thelearning results.

Example Process

FIG. 4 illustrates an example method 400 for processing point cloudsthrough multi-domain neighborhood embedding and weighting. FIG. 4 isdescribed in the context of the MNEW module 114-1, the automotive system104, and the vehicle 102. The method 400 is shown as sets of operations(or acts) performed, but not necessarily limited to the order orcombinations in which the operations are shown herein. Further, any ofone or more of the operations may be repeated, combined, or reorganizedto provide other methods. In portions of the following discussion,reference may be made to elements of the FIGS. 1 through 3 , referenceto which is made for example only. The techniques are not limited toperformance by one entity or multiple entities.

At 402, point cloud data is obtained from one or more sensors of avehicle. For example, the automotive system 104 includes the processor108 and receives, via the sensor interface 112, and from the sensors106, point cloud data captured by the sensors 106 as the vehicle 102 isperforming a vehicle operation (e.g., autonomously navigating down aroadway).

At 404, geometric properties of the point cloud data are determined. Forexample, the MNEW module 114 executes on the processor 108 and bygrouping geometry neighbors in the point cloud data, the MNEW module 114produces the geometry neighborhood domain 202-1.

At 406, feature properties of the point cloud data are determinedincluding feature similarities of the points. For example, the MNEWmodule 114 executes on the processor 108 and by through grouping featureneighbors based on similarities (e.g., using multiple k-nearestneighbors algorithms) that change across different network layers, theMNEW module 114 generates a feature neighborhood domain 202-2.

At 408, the point cloud data is modified by embedding a respectiveweighting at each point in the point cloud data. For example, theprocessor determines a sparsity for each point in the two domains 202-1and 202-2. This can include determining a geometric sparsity of thatpoint in the geometry neighborhood domain and determining a featuresparsity of that point in the feature neighborhood domain. The geometrysparsity 204-1 and the feature sparsity 204-2 at each point istransformed into the weighting factors 206-1 and 206-2, which areapplied to the raw point cloud data from the raw data store 116. Eachrespective weighting is based on the geometric sparsity and the featuresparsity, rather than only being based on one or the other, whichimproves object detections and classifications.

The method 400, can be repeated to improve its effectiveness inaccurately weighting point cloud data. This can include recomputingweightings to be applied to the point cloud data. Feature properties ofthe modified point cloud data are determined, as are geometricproperties of the modified point cloud data. The processor 108 canfurther modify previously modified point cloud data using new weightingsbased in part on the geometric properties of the modified point clouddata and the feature properties of the modified point cloud data.

At 410, a vehicle operation is executed using the respective weightingsembedded in the modified point cloud data. For example, the automotivesystem 104 performs a vehicle function based on objects detected fromthe segmented point cloud data store 118. With weightings embedded inthe data, the automotive system 104 can use sparsely populated pointcloud data to confidently identify and classify objects and otherobstacles that would otherwise impede the vehicle 102 from performing anautonomous or semi-autonomous perception-navigation task.

Example Point Cloud Data

FIG. 5-1 illustrates an example of a dense point cloud 500-1 processedby an automotive system configured to process point clouds usingmulti-domain neighborhood embedding and weighting. FIG. 5-2 illustratesan example of a sparse point cloud 500-2 processed by an automotivesystem configured to process point clouds using multi-domainneighborhood embedding and weighting. FIGS. 5-3 and 5-4 are grey scaleversions 500-3 and 500-4 of the point clouds 500-1 and 500-2 shown inFIGS. 5-1 and 5-2 , respectively. FIGS. 5-5 and 5-6 are black and whiteversions 500-5 and 500-6 of the point clouds shown in FIGS. 5-1 and 5-2, respectively.

Example Performance

FIG. 6-1 illustrates an example point cloud distribution and performancevariation by distance. FIG. 6-2 illustrates an example point clouddistribution and performance variation by sparsity. In particular, whenapplied to the application of autonomous driving task, the MNEW 114-1can segment even sparse point cloud data at the raw data store 116.FIGS. 6-1 and 6-2 show the effectiveness of the MNEW module 114-1 forpoints at different distances (e.g., with respect to a LiDAR sensor asone of the sensors 106) and sparsity (with respect to nearby points).

In FIGS. 6-1 and 6-2 , distribution histograms of distance and sparsityare demonstrated, and performance variations are investigated. Observein FIG. 6-1 , there exists an abrupt performance elevation atapproximately fifty meters distance. To be explicit, points at fardistances (e.g., greater than fifty meters) may be limited in quantitybut are generally involved with well-learned classes (e.g., road,building, terrain) which contains relatively higher percentages ofsamples.

In FIG. 6-2 , what is shown, is as the sparsity increases, theperformance of the MNEW module 114-1 starts to decrease until 0.7˜0.8and then increases. This is associated with the sparsity distributionhistogram, implying that the number of samples affect the performance.

Because the sparsity distribution for dense datasets is more even thanfor sparse datasets, the MNEW module 114-1 infers information thatimproves the effectiveness of the data. For the neighborhood selection,the MNEW module 114-1 compares the neighbors collected by geometrylocation and feature similarity, in both domains. For the attention andweighting, the MNEW module 114-1 may optionally enable the factor ofneighbor distance and local sparsity in geometry and feature domain, andoptionally set them as fixed values (D_(g), D_(f), S_(g), S_(f)) ortransform them.

Some additional examples of implementing multi-domain neighborhoodembedding and weighting for point cloud processing are described in thefollowing section of examples.

Example 1. A method comprising: obtaining point cloud data from one ormore sensors of a vehicle; determining geometric properties of the pointcloud data by at least: determining geometric distances between pointsin the point cloud data; and determining a sparsity of the point clouddata at each of the points; determining feature properties of the pointcloud data including feature similarities of the points; modifying thepoint cloud data by embedding a respective weighting at each point inthe point cloud data, the respective weighting being based on: thegeometric distances that are between that point and other points in thepoint cloud data; the sparsity of the point cloud data at that point;and the feature similarities of that point and the other points in thepoint cloud data; and responsive to modifying the point cloud, executinga vehicle operation using the respective weightings embedded in themodified point cloud data.

Example 2. The method of any preceding example, wherein the point clouddata includes a same quantity of sampled points after modifying thepoint cloud data as before modifying the point cloud data.

Example 3. The method of any preceding example, wherein the point clouddata comprises sparse data.

Example 4. The method of any preceding example, wherein the point clouddata comprises dense data.

Example 5. The method of any preceding example, wherein determining thegeometric properties of the point cloud data comprises grouping geometryneighbors in the point cloud data to produce a geometry neighborhooddomain.

Example 6. The method of any preceding example, wherein determining thefeature properties of the point cloud data comprises grouping featureneighbors in the point cloud data to produce a feature neighborhooddomain.

Example 7. The method of any preceding example, further comprising:determining the sparsity of the point cloud data at that point, whereinthe sparsity is determined by: determining a geometric sparsity of thatpoint in the geometry neighborhood domain; and determining a featuresparsity of that point in the feature neighborhood domain, wherein therespective weighting is further based on the geometric sparsity and thefeature sparsity.

Example 8. The method of any preceding example, further comprising:determining geometric properties of the modified point cloud data;determining feature properties of the modified point cloud data;modifying the modified point cloud data using weighting based in part onthe geometric properties of the modified point cloud data and thefeature properties of the modified point cloud data.

Example 9. A system comprising means for performing the method of any ofthe preceding examples.

Example 10. A computer readable storage medium comprising instructionsthat, when executed, configure a processor to perform the method of anyof the preceding examples.

Conclusion

While various embodiments of the disclosure are described in theforegoing description and shown in the drawings, it is to be understoodthat this disclosure is not limited thereto but may be variouslyembodied to practice within the scope of the following claims. From theforegoing Description, it will be apparent that various changes may bemade without departing from the spirit and scope of the disclosure asdefined by the following claims.

The use of “or” and grammatically related terms indicates non-exclusivealternatives without limitation unless the context clearly dictatesotherwise. As used herein, a phrase referring to “at least one of” alist of items refers to any combination of those items, including singlemembers. As an example, “at least one of: a, b, or c” is intended tocover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination withmultiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b,a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b,and c).

What is claimed is:
 1. A method comprising: determining geometric properties of sampled data obtained from one or more sensors of a vehicle by at least determining geometric distances between points in the sampled data; determining feature properties of the sampled data including feature similarities of the points; modifying the sampled data to retain a same quantity of points that are each embedded with a respective weighting, the respective weighting being based on: the geometric distances that are between that point and other points in the sampled data; a sparsity of the sampled data at that point; and the feature similarities of that point and the other points in the sampled data; and responsive to modifying the sampled data, using the respective weightings embedded in the modified sampled data for enabling execution of a vehicle operation.
 2. The method of claim 1, wherein the sampled data includes a same quantity of sampled points after modifying the sampled data as before modifying the sampled data.
 3. The method of claim 1, wherein the sampled data comprises sparse data.
 4. The method of claim 1, wherein the sampled data comprises dense data.
 5. The method of claim 1, wherein determining the geometric properties of the sampled data comprises grouping geometry neighbors in the sampled data to produce a geometry neighborhood domain.
 6. The method of claim 5, wherein determining the feature properties of the sampled data comprises grouping feature neighbors in the sampled data to produce a feature neighborhood domain.
 7. The method of claim 6, further comprising: determining the sparsity of the sampled data at that point, wherein the sparsity is determined by: determining a geometric sparsity of that point in the geometry neighborhood domain; and determining a feature sparsity of that point in the feature neighborhood domain, wherein the respective weighting is further based on the geometric sparsity and the feature sparsity.
 8. The method of claim 1, further comprising: determining geometric properties of the modified sampled data; determining feature properties of the modified sampled data; modifying the modified sampled data using weighting based in part on the geometric properties of the modified sampled data and the feature properties of the modified sampled data.
 9. A system comprising at least one processor configured to: determine geometric properties of sampled data obtained from one or more sensors of a vehicle by at least determining geometric distances between points in the sampled data; determine feature properties of the sampled data including feature similarities of the points; modify the sampled data to retain a same quantity of points that are each embedded with a respective weighting, the respective weighting being based on: the geometric distances that are between that point and other points in the sampled data; a sparsity of the sampled data at that point; and the feature similarities of that point and the other points in the sampled data; and responsive to modifying the sampled data, using the respective weightings embedded in the modified sampled data for enabling execution of a vehicle operation.
 10. The system of claim 9, wherein the sampled data includes a same quantity of sampled points after modifying the sampled data as before modifying the sampled data.
 11. The system of claim 9, wherein the sampled data comprises sparse data.
 12. The system of claim 9, wherein the sampled data comprises dense data.
 13. The system of claim 9, wherein the at least one processor is configured to determine the geometric properties of the sampled data comprises grouping geometry neighbors in the sampled data to produce a geometry neighborhood domain.
 14. The system of claim 13, wherein the at least one processor is configured to determine the feature properties of the sampled data comprises grouping feature neighbors in the sampled data to produce a feature neighborhood domain.
 15. The system of claim 14, wherein the at least one processor is further configured to: determine the sparsity of the sampled data at that point, wherein the sparsity is determined by: determine a geometric sparsity of that point in the geometry neighborhood domain; and determine a feature sparsity of that point in the feature neighborhood domain, wherein the respective weighting is further based on the geometric sparsity and the feature sparsity.
 16. The system of claim 9, wherein the at least one processor is further configured to: determine geometric properties of the modified sampled data; determine feature properties of the modified sampled data; modify the modified sampled data using weighting based in part on the geometric properties of the modified sampled data and the feature properties of the modified sampled data.
 17. A non-transitory computer-readable storage medium comprising instructions that when executed configure a processor to: determine geometric properties of sampled data obtained from one or more sensors of a vehicle by at least determining geometric distances between points in the sampled data; determine feature properties of the sampled data including feature similarities of the points; modify the sampled data to retain a same quantity of points that are each embedded with a respective weighting, the respective weighting being based on: the geometric distances that are between that point and other points in the sampled data; a sparsity of the sampled data at that point; and the feature similarities of that point and the other points in the sampled data; and responsive to modifying the sampled data, using the respective weightings embedded in the modified sampled data for enabling execution of a vehicle operation.
 18. The computer-readable storage medium of claim 17, wherein the sampled data includes a same quantity of sampled points after modifying the sampled data as before modifying the sampled data.
 19. The computer-readable storage medium of claim 17, wherein the sampled data comprises sparse data.
 20. The computer-readable storage medium of claim 17, wherein the sampled data comprises dense data. 